Abstract
Speckle suppression plays an important role in improving ultrasound (US) image quality. While lots of algorithms have been proposed for 2D US image denoising with remarkable filtering quality, there is relatively less work done on 3D ultrasound speckle suppression, where the whole volume data rather than just one frame needs to be considered. Then, the most crucial problem with 3D US denoising is that the computational complexity increases tremendously. The nonlocal means (NLM) provides an effective method for speckle suppression in US images. In this paper, a programmable graphic-processor-unit- (GPU-) based fast NLM filter is proposed for 3D ultrasound speckle reduction. A Gamma distribution noise model, which is able to reliably capture image statistics for Log-compressed ultrasound images, was used for the 3D block-wise NLM filter on basis of Bayesian framework. The most significant aspect of our method was the adopting of powerful data-parallel computing capability of GPU to improve the overall efficiency. Experimental results demonstrate that the proposed method can enormously accelerate the algorithm.
Highlights
Ultrasonic imaging owns advantages such as noninvasive, radiation-free, low-cost, and fast imaging compared with other medical imaging techniques [1]
An efficient 3D ultrasound image denoising algorithm is in urgent need in the field of 3D ultrasound
In order to testify the performance of the proposed algorithm over to the traditional nonlocal means (NLM), comparisons were made on the US fetus data as it involves more detail information
Summary
Ultrasonic imaging owns advantages such as noninvasive, radiation-free, low-cost, and fast imaging compared with other medical imaging techniques [1]. It has been widely used in many medical applications. Since 3D ultrasound imaging can provide clearer spatial relationship and more abundant diagnostic information compared with 2D ultrasound, it attracts much attention from the related fields. Speckle could obscure the important image details and reduce the contrast of the soft tissues in the image, thereby causing great difficulties to the subsequent US image processing such as edge detection, image segmentation, and image registration. An efficient 3D ultrasound image denoising algorithm is in urgent need in the field of 3D ultrasound
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